Intermittent plankton in marine turbulence

Transcription

1 François G. Schmitt CNRS Research professor (DR) Director of the LOG, Laboratory of Oceanology and Geosciences, Wimereux, France Intermittent plankton in marine turbulence Leiden, Feb. 2016, Lorentz Center 1

2 Laboratory of Oceanology and Geosciences - LOG, Wimereux Old station (1874) but new CNRS laboratory created in 2008: 60 researchers 30 PhD students 130 members Interdisciplinary oceanology studies: physics, ecology, geosciences Activities as a marine station: Research Teaching Hosting (students, researchers) Observation Interdisciplinary research: observation/experimentation/modelization specialized in coastal research from bacteria to satellites from microscales to climate 2

3 Outline 1- Intermittent fluorescence: field studies 2- Copepod complex behavior: lab studies

4 Scaling approach in turbulence Fluctuations of a passive scalar depend on scale. Dissipation of the scalar variance (analogy with dissipation of velocity variance=k). Still 1/3 scaling law

5 Scaling and intermittency in oceanic turbulence Theoretical results confronted with field studies Ocean' turbulence' is' not' a' well1controlled' system' as' the' laboratory:' complex' system,' many'forcing,'huge'reynolds'number ' However'it'is'in'the'Ocean'that'the'first'convincing'and'large1Re'measurements'showed' K41:'Grant,'Stewart'and'Moilliet'1962'classical'paper From Monin and Yaglom

6 Turbulence and phytoplankton: early studies Comparison with a passive scalar: find a 5/3 scaling law

7 Simultaneous measurements Fixed point measurements: temperature and fluorescence 2 Hz simultaneous measurements, anchor station, Eastern English Channel Seuront, Schmitt, Lagadeuc, Schertzer, Lovejoy, Frontier, Geophys. Res. Lett., 1996 Seuront, Schmitt, Lagadeuc, Schertzer, Lovejoy, J. Plankton Res., 1999

8 Simultaneous measurements Fixed point measurements: temperature and fluorescence Multifractal scaling analysis of patchiness / intermittency / heterogeneities Classical structure functions analysis Scaling exponents, nonlinear indicating intermittency Temperature: close to theory (passive scalar) for scales between 1s and 1000 s. Fluorescence (phytoplankton): - scales between 1 and 20 s: close to temperature. Passive scalar. Physics dominates. - Scales > 20s: different scaling law. Biologically active scalar. Growth, sinking, community interactions?

9 Automatic sampling Automa'c)measurements,)every)20)minutes)(since)2004))of:) Water'temperature' Air'temperature' Salinity' Fluorescence' Turbidity' Dissolved'oxygen'concentraOon' PAR' Water'level' Air'relaOve'humidity' Wind'direcOon'and'magnitude

10 Automatic sampling Temperature Fluorescence Temperature Fluorescence Phaeocystis globosa spring blooms 10

11 Automatic sampling Temperature Fluorescence Temperature Fluorescence Phaeocystis spring blooms Power'law'PDF'of'fluorescence' Slope'12:'Cauchy'law' Derot, Schmitt, Gentilhomme, Cont. Shelf Res., 2015 Power' law' scaling' power' spectrum,' slope'1.2'for'fluorescence'(universal?)' Different'from'temperature'spectrum 11

12 Automatic sampling L4 station from NERC (UK) 10 parameters automatic measurements 1 hour resolution Power'law'PDF'of'fluorescence' Slope'13.7' Power' law' scaling' power' spectrum,' slope'1.2'for'fluorescence'(universal?)' Different'from'temperature'spectrum 12

13 Dynamics:)Empirical)mode)decomposi'on)applied)to)the) fluorescence)'me)series Earthquake*like- behavior:- during' bloom' events,' large' fluctuaoons' are' visible,' having' long1range' correlaoons' and' high' intensity' (power1law,' like' the' Gutemberg1Richter'law'for'earthquakes) Derot, Schmitt, Gentilhomme, Cont. Shelf Res.,

14 Satellite chlorophylle images and turbulence Chlorophylle estimation from satellite observations Larger scale turbulence influence

15 Heterogeneities in satellite images Satellite'reflectance'data'are'non1linearly'transformed'into'a'proxy'of''temperature'and' Chlorophylle'a'concentraOon Chl-a SST Different' parameters'sampled' from'angola' Namibia'Coastal' region'on'301 April12013 Missing'data:'cloud'coverage' Irregular'images'with'large'fluctuaOons'(here'log1scale'for'Chl'a'on'the'le_)'

16 Use of 2D structure functions Time series: structure functions scale invariant property of structure functions Images: 2D structure functions t M t+t scale invariant property of 2D structure functions N Renosh, Schmitt, Loisel, PLoS ONE, 2015

17 Applications to images: test using fbm Images: 2D structure functions M scale invariant property of 2D structure functions N Fractional Brownian motion: theoretical stochastic process for depending on a parameter 0<H<1, with known properties Renosh, Schmitt, Loisel, PLoS ONE, 2015

18 Applications to images: test using fbm Fractional Brownian motion: theoretical stochastic process for depending on a parameter 0<H<1, with known properties Valida'on)of)this)methodology Renosh, Schmitt, Loisel, PLoS ONE, 2015

19 2D structure functions: Applications to satellite images Images: 2D structure functions M scale invariant property of 2D structure functions N Generalization to a nonlinear moment function (moment of order q) to characterize the full range of intermittency parameters Test on the number of points needed to have a good convergence Works even with missing data Renosh, Schmitt, Loisel, PLoS ONE, 2015

20 Scaling analysis of satellite images Chl a from space MODIS images Renosh, Schmitt, Loisel, PLoS ONE, 2015

21 Going below Kolmogorov scale Intermediate dissipation range Frisch and Vergassola 1991 In the framework of intermittent multifractal velocity field, there are singularities 0.15<h<0.4 Kolmogorov scale corresponds to a mean singularity h=1/3 A singularity h is associated to a new dissipation scale: small fluctuations gives: medium fluctuations With realistic values of the Reynolds number, largest fluctuations can go to 1/3 of the Kolmogorov scale large fluctuations

22 Copepod behavior

23 Scales Molecules Par'cles Viruses Bacteria Phyto Zoo Quantum physics (molecules, atoms) Elements)of) fluid Viscous fluids Kolmogorov) scale Fluid mechanics Turbulence

24 Particulate Reynolds number for swimming animals Particle Reynolds number versus size for marine animals, from bacteria to whales (data from the literature, personal compilation F. Schmitt) Scaling law for particles Reynolds number, over 7 orders of magnitude in scale and 13 orders of magnitude in Re Re_p=1 for a size of 0.6 mm (copepod typical size)

25 Particulate Reynolds number for swimming animals Naganuma (1996) and Yen (2000): copepods use the advantage of living on the border of different worlds: low Re for feeding, large Re for escape

26 Copepods live in a turbulent world Copepods are small crustaceans, eating phytoplankton and prey of fish larvae: important position in the food web Their size is often similar to Kolmogorov s scale They live in a turbulent world, at the limit with viscous world They see the flow in a Lagrangian way

27 Copepods can swim Copepods belong to zooplankton. 11,500 species. Plankton = passively advected by the flow In fact since the end of the 1970s copepod swimming abilities are recognized and recorded: not so passive as previously assumed Oldest copepod records originate from the Cambrian period (500 million years) (Selden et al. 2010). With 3 generation per year, this makes generations. They have a perfect adaptation to their turbulent environment. Moving needs energy. They need to move to reproduce, to find food and to avoid predators. Hence a complex behavior, which is genetically determined.

28 Copepods can swim Swimming behavior is hence often studied by considering copepods in still water, with cameras in dark conditions (to avoid phototropism). Infrared cameras 25 frames per second (fps) Trajectory extraction

29 Copepods can swim Bundy et al., 1993 Centropages velificatus Schmitt and Seuront, 2001 Temora longicornis

30 Trajectories are not Brownian Increments between successive steps: large values A Time (s) Brownian Scaling models: Brownian motion: linear Fractional Brownian motion: linear Lévy walk: bilinear Multifractal random walk: nonlinear Lévy walk In fact the particle diffusion is not normal due to large jumps. These jumps are done by the antennas. Copepod data Schmi`'and''Seuront:'MulOfractal'random'walk'in'copepod'behaviour.'Physica-A,-301,'114,' ,'2001

31 Behavior studied using symbolic dynamics Symbolic dynamics of swimming states Clustering: transformation of a continuous system into discrete states, with finite number of symbols. Study of the dynamics of symbols. Here symbols are swimming states. Usually: pause low velocity jumps. Study of residence times in each state, and transition probabilities between states. Example: Schmi`'et'al.,'Physica'A,'2006' Moison,'Schmi`,'Souissi,'Seuront,'Hwang,'Symbolic'dynamics'and'entropies'of'copepod'behaviour' under'non1turbulent'and'turbulent'condioons,'journal-of-marine-systems'77,' ,'2009

32 Copepod behavior studies Such approach, using trajectory analysis, or the dynamics of swimming states, has been done in many studies Different species Males/females Development state (nauplii-copepodids) Influence of food (phytoplankton concentration) Characteristic of the medium (temperature, salinity, turbulence) Mating behavior Influence of predators Influence of pollutants Daphnia behavior is used to monitor fresh water quality. Same could be done using copepod behavior to characterize marine water quality: very sensitive to small concentrations of pollutants. Our works (around 30 papers in this topic in our lab):

33 Copepod acceleration Arbitrary-order Hilbert Spectral Analysis Most behavior studies are done mainly using cameras at classical fps: 25 to 50 fps (Strickler 1975 used 250 fps). A powerful multiscale analysis However, copepod jumps are very fast and several studies have used high speed cameras to better capture the jumps. method

34 Copepod jumps First high speed camera study of copepods fps. Hydromechanial stimulii. Acartia tonsa. Acartia lilljeborgii. Localized events. Response latency 4 to 5 ms Max. jump speed 50 to 60 cm/s Max acceleration 140 to 220 m/s^2 Jump duration 24 to 28 ms Distance jumped 6 mm Buskey,'Lenz,'Hartline,'2002

35 Copepod jumps Influence of light 1000 fps. Acartia tonsa. Localized events. Response latency 6 to 7 ms Max. jump speed 26 to 30 cm/s Max acceleration 90 to 130 m/s^2 Jump duration 44 to 74 ms Distance jumped 8 to 12 mm Buskey'&'Hartline,'2003

36 Copepod jumps Sequence of escape from a predator 1000 fps. Acartia tonsa. Buskey'et'al.,'2011 Localized events. Peak velocity 65 cm/s Peak acceleration 250 m/s^2, close to 25G!

37 A simple interpretation of this acceleration of 20G d d: detection distance V: velocity difference a: acceleration difference The fish larvae has a larger approaching velocity but smaller acceleration than the copepod No capture: the distance stays positive if a>v 2 /2d d = 3 mm V = 1 m/s fast approaching predator, or succion speed + velocity a > 25 G Consistent with measured values If captured: the time is V/a=5 ms very short time For a jump of duration 10 ms, jumped distance of the order of 1 cm

38 Acceleration experiment Some'preliminary'results'from'a'master'work'(I.)Benkeddad).' Some'data'analyses'done'in'collaboraOon'with'H.'Ardeshiri'(PhD' student'with'myself'and'e.'calzavarini)

39 Acceleration experiment Phantom Miro ex2 Vision Research 640 x 480 pixels, up to 1240 fps exposure time 5 microsecond CMOS sensor. 4 Go RAM. A simple alternate light system to generate copepod jumps

40 Acceleration experiment Trajectory extraction and analysis using the software TEMA Trajectory Velocity (x) Acceleration (x)

41 Acceleration experiment 2 species fed by a red algae (Rhodomonas baltica): Eurytemora affinis and Acartia tonsa (males and females) E. affinis (male) A. tonsa (male) Trajectory Copepod culture Phytoplankton culture Velocity (x) Acceleration (x) 1000 fps. Resolution of 512 x 384 pixels. 18 C, salinity of 15 for E. affinis and 30 for A. tonsa.

42 Acceleration experiment 163 trajectory samples analyzed, for all 4 types (2 species, and M and F). 3 million images 1 To data. Trajectory Velocity (x) Acceleration (x) After the films, copepods are fixed (in alcohol) and later their size is measured using a microscope

43 Acceleration experiment Short portion of a trajectory Zooms showing sampling times (dots)

44 Acceleration experiment 2 components of the velocity 2 components of the acceleration

45 Acceleration experiment 2 components of the velocity 2 components of the acceleration

46 Acceleration experiment velocity: jump event acceleration: jump event 20 ms

47 Acceleration experiment Maximum acceleration recorded for each individual Females are bigger than males No clear size effect for the maximum acceleration Animal size Acceleration values up to 25G

48 Acceleration experiment Other results Generic shape of jump velocity events, used to model copepods in a Direct Numerical Simulation of Navier Stokes (homogeneous turbulence) PhD thesis of H. Ardeshiri Ardeshiri, Benkeddad, Schmitt, Souissi, Toschi, Calzavarini: A lagrangian model of copepod dynamics: clustering by escape jumps in turbulence, submitted to Physical Review E.

49 Conclusion Field'studies'confronted'with'theories:'even'if'the'ocean'is'a'complex' system,'extracong'some'scaling'laws'is'possible' Generality'of'some'laws'found'here,'to'be'tested'in'other'situaOons' Zooplankton'behavior:'complexity'that'can'be'tackled'to'infer'some' informaoons'on'the'state'of'the'animals'or'of'their'surrounding' An'important'step'is'to'be'able'to'record'trajectories'in'turbulence' AcceleraOon:'could'be'important'for'the'ecology'of'these'species.' More'info:' h`p:// 49

50 It is the end Adver&sement:'' Just'released,'Cambridge'University'Press'(2016) 50